Human Robot Collaboration (HRC) allows humans to operate more efficiently by reducing their human effort. Robots can do the majority of difficult and repetitive activities with or without human input. There is a risk of accidents and crashes when people and robots operate together closely. In this area, safety is extremely important. There are various techniques to increase worker safety, and one of the ways is to use Augmented Reality (AR). AR implementation in industries is still in its early stages. The goal of this study is to see how employees' safety may be enhanced when AR is used in an HRC setting. A literature review is carried out, as well as a case study in which managers and engineers from Swedish firms are questioned about their experiences with AR-assisted safety. This is a qualitative exploratory study with the goal of gathering extensive insight into the field, since the goal is to explore approaches for AR to improve safety. Inductive qualitative analysis was used to examine the data. Visualisation, awareness, ergonomics, and communication are the most critical areas where AR may improve safety, according to the studies. When doing a task, augmented reality aids the user in visualizing instructions and information, allowing them to complete the task more quickly and without mistakes. When working near robots, AR enhances awareness and predicts mishaps, as well as worker trust in a collaborative atmosphere. When AR is utilized to engage with collaborative robots, it causes less physical and psychological challenges than when traditional approaches are employed. AR allows operators to communicate with robots without having to touch them, as well as make adjustments. As a result, accidents are avoided and safety is ensured. There is a gap between theoretical study findings and data gathered from interviews in real time. Even though AR and HRC are not new topics, and many studies are being conducted on them, there are key aspects that influence their adoption in sectors. Due to considerations such as education, experience, suitability, system complexity, time, and technology, HRC and AR are employed less for assuring safety in industries by managers in various firms. In this study, possible future solutions to these challenges are also presented.

Explosions of gas and dust continue to be recognized as an extreme danger in underground coal mines and still occur despite significant technological advances. Mining researchers have been attempting to accurately measure and quantify ventilation and gas properties since early mining; however basic monitoring attempts were limited by the available technologies. Recent advancements in monitoring and communication technologies enable comprehensive atmospheric monitoring to become feasible on a mine-wide scale. Atmospheric monitoring systems (AMS) allow operators to monitor conditions underground in real-time. Real-time monitoring enables operators to detect and identify developing high risk areas of the mine, as well as quickly alert mining personnel underground. Real-time monitoring also can determine whether conditions are safe for mining, to operate ventilation systems more efficiently, and to provide an additional layer of monitoring atmospheric conditions underground. AMS utilizes numerous monitoring technologies that will allow underground coal mines to comprehensively monitor gas and ventilation parameters. AMS are utilized worldwide as well as in the United States, and can be modified to cater to specific hazards at different mines. In the United States, AMS are primarily used to monitor belt lines and electrical installations for smoke, CO, and CH₄, and to automatically alarm at set thresholds. The research in this study investigates and analyzed AMS across the world (specifically Australia, Canada, and United States). Two case studies presented in Chapter 5 focus on the utilization and implementation of AMS in two underground coal mines in the United States. These case studies identify challenges regarding installation, data management, and analysis of real-time atmospheric monitoring data. The second case study provides significant evidence that correlates mine ventilation fan outages and changes in barometric pressure to increases in methane from previous works. This research does not attempt to quantify data, but intends to provide engineers knowledge to utilize, design, and implement an AMS. Several incident scenarios are simulated using ventilation computer software, as well as the benefits of monitoring in past disasters are analyzed. This research does not intend to place blame, but intends to increase the understanding of utilizing and implementing AMS in underground coal mines. / Ph. D.

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The occurrence of multiple fatality events in the U.S. underground coal mining industry, such as the Upper Big Branch mine explosion, illustrates the need for improved methods of major safety hazard identification and control. While many solutions to reducing the risk of mine disasters have been proposed, including stricter regulation and improved technology, a comprehensive risk management approach has yet to be fully integrated in the U.S. mining industry. Comprehensive risk management systems have been developed and implemented across a multitude of heavy industries, most notably the Australian minerals industry. This research examines the successful application of risk management in these industries, along with barriers towards U.S. implementation of risk management, which include the existence of competing safety models (e.g. behavior-based safety) and compliance regulation which consumes company resources, and limits incentive for beyond compliance safety measures. Steps towards the risk-based approach, including increased regulatory pressure and proactive initiation by high-ranking industry individuals, begin with the development of risk-based knowledge within the U.S. mining community. This research reviews the development of mine safety regulation in the U.S., and identifies regulatory constraints which have affected the diffusion of risk management. The development of a risk-based online platform which could complement the existing safety systems of U.S. underground coal operations, based on the Australian RISKGATE tool, is the central work of this research. This online platform has been developed by the research participants and industry professionals whose total underground coal mining experience exceeds 1,290 years. This joint effort has yielded a body-of-knowledge which may be used as a complementary safety control reference for U.S. mine operators who wish to employ risk management policies and practices at their own operations, or identify gaps within their own safety control systems. / Master of Science

Yes / OBJECTIVES: In the English National Health Service, the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient's risk of death. We examine the extent to which the accuracy of NEWS for predicting mortality could be improved by enhanced computer versions of NEWS (cNEWS). DESIGN: Logistic regression model development and external validation study. SETTING: Two acute hospitals (YH-York Hospital for model development; NH-Northern Lincolnshire and Goole Hospital for external model validation). PARTICIPANTS: Adult (≥16 years) medical admissions discharged over a 24-month period with electronic NEWS (eNEWS) recorded on admission are used to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) using the first electronically recorded NEWS (model M0) versus a cNEWS model which included age+sex (model M1) +subcomponents of NEWS (including diastolic blood pressure) (model M2). RESULTS: The risk of dying in-hospital following emergency medical admission was 5.8% (YH: 2080/35 807) and 5.4% (NH: 1900/35 161). The c-statistics for model M2 in YH for predicting mortality (in-hospital=0.82, 24 hours=0.91, 48 hours=0.88 and 72 hours=0.88) was higher than model M0 (in-hospital=0.74, 24 hours=0.89, 48 hours=0.86 and 72 hours=0.85) with higher Positive Predictive Value (PPVs) for in-hospital mortality (M2 19.3% and M0 16.6%). Similar findings were seen in NH. Model M2 performed better than M0 in almost all major disease subgroups. CONCLUSIONS: An externally validated enhanced computer-aided NEWS model (cNEWS) incrementally improves on the performance of a NEWS only model. Since cNEWS places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated to determine if it can improve care in hospitals that have eNEWS systems. / This research was supported by the Health Foundation. The Health Foundation is an independent charity working to improve the quality of healthcare in the UK. This research was also supported by the National Institute for Health Research (NIHR) Yorkshire and Humberside Patient Safety Translational Research Centre (YHPSTRC).

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